mask_rcnn_mae_adapter_base_lsj_fpn_25ep_coco.py

# Copyright (c) Shanghai AI Lab. All rights reserved.
_base_ = [
    '../_base_/models/mask_rcnn_r50_fpn.py',
    '../_base_/datasets/coco_instance.py',
    '../_base_/schedules/schedule_1x.py',
    '../_base_/default_runtime.py'
]
# pretrained = 'https://dl.fbaipublicfiles.com/mae/pretrain/mae_pretrain_vit_base.pth'
pretrained = 'pretrained/mae_pretrain_vit_base.pth'
model = dict(
    backbone=dict(
        _delete_=True,
        type='ViTAdapter',
        patch_size=16,
        embed_dim=768,
        depth=12,
        num_heads=12,
        mlp_ratio=4,
        drop_path_rate=0.1,
        conv_inplane=64,
        n_points=4,
        deform_num_heads=12,
        cffn_ratio=0.25,
        deform_ratio=0.5,
        use_extra_extractor=False,
        layer_scale=False,
        interaction_indexes=[[0, 2], [3, 5], [6, 8], [9, 11]],
        window_attn=[True, True, False, True, True, False,
                     True, True, False, True, True, False],
        window_size=[14, 14, None, 14, 14, None,
                     14, 14, None, 14, 14, None],
        pretrained=pretrained),
    neck=dict(
        type='FPN',
        in_channels=[768, 768, 768, 768],
        out_channels=256,
        num_outs=5,
        norm_cfg=dict(type='MMSyncBN', requires_grad=True)),
    rpn_head=dict(num_convs=2),
    roi_head=dict(
        bbox_head=dict(type='Shared4Conv1FCBBoxHead',
                       norm_cfg=dict(type='MMSyncBN', requires_grad=True)),
        mask_head=dict(norm_cfg=dict(type='MMSyncBN', requires_grad=True)),
    ))
# optimizer
img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)

# augmentation strategy originates from DETR / Sparse RCNN
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
    dict(
        type='Resize',
        img_scale=(1024, 1024),
        ratio_range=(0.1, 2.0),
        multiscale_mode='range',
        keep_ratio=True),
    dict(
        type='RandomCrop',
        crop_type='absolute_range',
        crop_size=(1024, 1024),
        recompute_bbox=True,
        allow_negative_crop=True),
    dict(type='FilterAnnotations', min_gt_bbox_wh=(1e-2, 1e-2)),
    dict(type='RandomFlip', flip_ratio=0.5),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='Pad', size=(1024, 1024)),
    dict(type='DefaultFormatBundle'),
    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
]

data = dict(train=dict(pipeline=train_pipeline))
lr_config = dict(
    _delete_=True,
    policy='CosineAnnealing',
    min_lr_ratio=0.01,
    warmup='linear',
    warmup_iters=2000,
    warmup_ratio=0.001)
runner = dict(type='EpochBasedRunner', max_epochs=25)
optimizer = dict(
    _delete_=True, type='AdamW', lr=0.0001, weight_decay=0.05,
    paramwise_cfg=dict(
        custom_keys={
            'level_embed': dict(decay_mult=0.),
            'pos_embed': dict(decay_mult=0.),
            'norm': dict(decay_mult=0.),
            'bias': dict(decay_mult=0.)
        }))
optimizer_config = dict(grad_clip=None)
fp16 = dict(loss_scale=dict(init_scale=512))
checkpoint_config = dict(
    interval=1,
    max_keep_ckpts=3,
    save_last=True,
)